Recent trends of NLP based text analytics (with specific to the semantic Information- medical application)
                     Research paper
RECENT TRENDS OF NLP BASED  
TEXT ANALYTICS (WITH SPECIFIC  
TO THE SEMANTIC INFORMATION- 
 MEDICAL APPLICATION)
Tags: Tutors India | Medical Application | Text Analytics | Data Mining | Text Mining | Pre-Processing
| Text Representation | NLP Techniques | Healthcare Sector | Syntactical Analysis | Semantic Analysis
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For example, considering U.S. 
 healthcare sector it has been To alleviate these issues 
The advancements in   noticed that 21-47% of the  employing  better decision-
medical sector have been expenditures occur due to  making  techniques on the basis 
 exhibited in different  inefficiency such of  information available can be  
technological aspects. inappropriate  use of considered to be an optimal  
antibiotics etc. solution.
BACK  With the advancements there  It was also observed that a  have also been noticed certain  small part of these costs was  
challenges such as higher related to low-quality care  
GROUND cost,  increase in complexity, due to which many people die poor  quality of care etc .  in the U.S. every year.
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I m p l e m e n t a t i o n  O f   
In format ion  T e c h n o l o g y
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While implementation of information technology has enabled  
gathering a vast amount of data, employing analytics tools.
Techniques has enabled retrieving the relevant or desired  
information from this unstructured data and then transforming 
 it for enabling better decision-making process. You will find the best assignments for future  
researchers enrolled in Engineering and  
Employing analytics tools and techniques has been of great Technology. These topics are researched 
 help for medical professionals in predicting and diagnosing in-  depth at the University of Glasgow, UK,
Sun Yat-sen University, University of 
 diseases.
St  Andrews and many more.
As per the estimates, applying data mining can enable saving
$450 billion every year from the U.S. medical sector.
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R E C O G N I T I O N  O F  
 B I O M E D I C A L
The primary task of the techniques used is
01 classification of papers into different categories 
 such that it can be fed into a database and thus  
retrieved by the end-user as required.
Tutors India has vast experience in  
developing assignment topics for 
Recognition of biomedical entities  
includes identifying and grouping the entity  02 students  pursuing the UK dissertation in 
names as per the different medical domains. computer  science. Order Now
There are numerous sources available, there is 
03  not a particular resource that can be considered  
as new medicines are explored every time and  
this imposes as the main hindrance for NLP.
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PRIMARY M E T H O D O L O G I E S  -   
A S S O C I A T I N G  M E D I C A L  E N T I T I E S
NLP offers three primary methodologies for associating the medical entities. They are 
 as follows:
•Linguistic-based approaches: It is based on using parsers to understand syntactic  
structures and associate them into semantic representations.
•Pattern-based approaches: It employs a set of patterns for associating the entities  
that are defined by domain experts.
•Machine Learning-based approaches: It is based on deriving associations in new  
categories that represent similar text.
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The Categorization process of processing 
followed  by NLP is divided into the following 
steps:
• Text Preprocessing/Tokenization
• Lexical Analysis
• Syntactical Analysis Categorizat ion
• Semantic Analysis o f  N L P
In order to identify the future assignment topics, we have  
reviewed the assignments (recent peer-reviewed 
topics)  on both engineering and management
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The healthcare sector is also  
Extraction of relevant data from Thus, considering this text  
concerned about extracting the 
 the unstructured data is another  mining technique is used to 
 relevant data from different  
challenge revolving around the   extract structured 
sources or the data which is  
healthcare or medical domain. information  from 
stored in different databases.
unstructured data.
Healthcare  Sector
In the last few years, the amount of  
Due to the presence of this unstructured 
information generated regularly in the field 
 data, the process of obtaining structured 
 of medicine is growing continuously,  
 or relevant data for decision-making  
especially the information produced by  
sometimes becomes more complex.
professionals in their day to day routine.
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Thus to conclude, it can be affirmed that the primary challenge encountered by different medical 
 professionals is not just retrieving any data.
The different phases involved for extracting useful medical data from the unstructured data are as 
 follows:
Pre-processing stage: In this stage, unstructured data is arranged as per the standards and 
 then processed further using NLP techniques.
Text representation stage: In this stage, unstructured data is converted into an appropriate 
 form which enables it to be analyzed efficiently.
Discovery stage: This is the last stage of processing unstructured data, and in this stage, the  
information expected by the end-user is obtained by employing techniques such as clustering, 
 classification etc.
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C O N C L U S I O N S
D A T A  ANALYTICS -  A P P L I C A T I O N S
The continuous advancement of data analytics and its applications to medicine has  
served to be a great support for the medical professionals and patients as well. Some of 
 these benefits are listed as follows:
Better quality of care Reduced medical Enabling prevention Facilitating the  Reducing the consumption
 services offered to   errors.  and detection of  availability of most    of both time 
patients. diseases in an  beneficial treatment as well as  
earlier stage.  for patients. expenditure incurred by  
healthcare management.
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